Supporting a Nation of Neighbors with Community Analysis Visualization Environment
National Science Foundation SoCS proposal IIS - 0968521
Computationally-mediated civic participation is emerging as a solution to contemporary economic and social issues such as healthcare, energy sustainability, education, environmental protection, and disaster response. Our research will study the socio-technical causes of success and failure in the community safety system, Nation of Neighbors, leading to potent predictive models that enable early interventions to shift the balance towards success.
The Community Analysis Visualization Environment (CAVE) will enable: 1) community managers will use a visual analytic toolkit to take the pulse of their communities, identifying generative, stable, inactive, and destructive components; 2) researchers will be able to compare across communities to understand the features of successful and failing communities.
The intellectual merit will be to develop and validate visual analytic tools for: (1) moderately-skilled online community managers to understand the dynamics of community network evolution so as to intervene effectively (2) skilled researchers to conduct cross-community analysis, (3) the 4-stage Reader-to-Leader Framework by studying community manager strategies to cope with the practical challenge of dramatically increased participation as well as threatening disruptions caused by external events, malicious attacks, harmful rumors, and disaffected members.
This research will have broader impact for many computationally-mediated civic participation systems such as for natural disasters (earthquakes, toxic waste discharges, etc.), medical outbreaks (food poisoning, flu, pandemics, etc.), and human threats (terrorists, serial killers, bombers, arsonists, etc.).
Supporting a Nation of Neighbors with Community Analysis Visualization Environment
National Science Foundation SoCS proposal IIS - 0968521
Computationally-mediated civic participation is emerging as a solution to contemporary economic and social issues such as healthcare, energy sustainability, education, environmental protection, and disaster response. Our research will study the socio-technical causes of success and failure in the community safety system, Nation of Neighbors, leading to potent predictive models that enable early interventions to shift the balance towards success.
The Community Analysis Visualization Environment (CAVE) will enable: 1) community managers will use a visual analytic toolkit to take the pulse of their communities, identifying generative, stable, inactive, and destructive components; 2) researchers will be able to compare across communities to understand the features of successful and failing communities.
The intellectual merit will be to develop and validate visual analytic tools for: (1) moderately-skilled online community managers to understand the dynamics of community network evolution so as to intervene effectively (2) skilled researchers to conduct cross-community analysis, (3) the 4-stage Reader-to-Leader Framework by studying community manager strategies to cope with the practical challenge of dramatically increased participation as well as threatening disruptions caused by external events, malicious attacks, harmful rumors, and disaffected members.
This research will have broader impact for many computationally-mediated civic participation systems such as for natural disasters (earthquakes, toxic waste discharges, etc.), medical outbreaks (food poisoning, flu, pandemics, etc.), and human threats (terrorists, serial killers, bombers, arsonists, etc.).
Ben Shneiderman (PI, Computer Science)
Alan Neustadtl (Co-PI, Sociology)
Catherine Plaisant (Co-PI, Human-Computer Interaction Laboratory)
Art Hanson (Nation of Neighbors)
Jee-hye Kang (Sociology)
PJ Rey (Sociology)
Awalin Sopan (Computer Science)
Nick Violi (Computer Science)